Drones are expected to support applications such as emergency response, parcel delivery, and infrastructure monitoring in dense urban airspaces, creating traffic levels that are unmanageable for human operators. Autonomous separation management is therefore essential, combining strategic and tactical control to prevent conflicts. This paper addresses the tactical landing phase by introducing a centralized landing flow manager—a reinforcement learning (RL) agent that adjusts drone speed and heading to merge landing flows safely and efficiently prior to a final approach fix. The objective of the work was to demonstrate the potential of reinforcement learning in this novel context, by implementing and evaluating it in simulation and testing its capabilities with 10 concurrent landing drones. The RL agent learns to successfully separate traffic, thereby lowering intrusion counts compared to the baseline autopilot, but is outperformed in safety by the decentralized Modified Voltage Potential (MVP) method due to outlier scenarios. Nevertheless, the RL-based system achieves faster scenario completion and thus a higher overall throughput, by speeding up the vehicles towards the final approach fix. Future work will explore improved network architectures, transfer learning across varied scenarios, and algorithmic fine-tuning to further enhance safety performance.

Abstract—In the coming decades, drones are expected to operate within urban areas at high volumes, and if implemented suc- cessfully, applications such as infrastructure inspection, medical supply and parcel delivery can be improved by the technology. This poses a challenge: how are these drones to be guided in this highly-constrained airspace? Many existing projects have approached the problem from different angles: some place more importance on the Tactical Layer and thus resolving conflicts in flight, while other research focuses on the Strategic Layer with scheduling or airspace design. While analysis is done on a complete system, with all separation management layers implemented, work remains to be done regarding quantifying how these layers interact, and what positive characteristics of these interactions can be utilised to make the system more efficient, safe, and robust to uncertainties. This paper proposes a framework on which this analysis can be performed. Firstly, lay- ers are investigated independently. A feedback system is proposed, where layer outputs are measured, as is the resulting system performance. For instance, an initial hypothesis is that reducing airspace complexity in the Strategic layer, while accounting for uncertainty, will lead to better overall system performance. This can help with minimising flight times and improving overall safety. Also, manoeuvres performed by the Tactical (in-flight) layer should take this complexity metric into account. The feedback loop approach also proposes that the complexity be fed back to the central planner, and that the Strategic (Pre-Flight) layer should be able to take system status into account when performing planning.

Reinforcement Learning (RL) is rapidly becoming a mainstay research direction within Air Traffic Management and Control (ATM/ATC). Many international consortia and individual works have explored its applicability to different ATC and U-Space / Urban Aircraft System Traffic Management (UTM) tasks, such as merging traffic flows, with varying levels of success. However, to date there is no common basis on which these RL techniques are compared, with many research parties building their own simulator and scenarios from scratch. This can diminish the value of this research, as the performance of an algorithm cannot be easily verified, or compared to that of other implementations. This hampers development in the long run. The gymnasium library shows for other research domains that this can be solved by providing a set of standardised environments, which can be used to test different algorithms, and compare them to benchmark results. This paper proposes BlueSky-Gym: a library that provides a similar set of test environments for the aviation domain, building on the existing open-source air traffic simulator BlueSky. The current BlueSky-Gym environments range from vertical descent environments, to static obstacle avoidance and traffic flow merging. Built upon the Gymnasium API and the BlueSky air traffic simulator, it delivers an open-source solution for the ATC-specific RL performance benchmark. In the initial release of BlueSky-Gym, 7 functional environments are presented. Preliminary experiments with PPO, SAC, DDPG and TD3 are presented in this paper. Results show stable training is obtained on all of the environments with the default hyperparameters. On some environments, there is a large performance gap, with the on-policy PPO often trailing, but overall no clear algorithm that outperforms others across the board in terms of total reward.

The consumer drone sector is expected to grow rapidly in the coming decades. In Europe alone, some predictions show as many as seven million drones will be flying by 2050. This poses a challenge for surveillance. In this paper, we study an Automatic Dependent Surveillance system concept similar to the one for current aircraft surveillance, which allows the drone to broadcast information about itself without external input. The study’s main contents are threefold. The first consists of recommendations made based on literature. Then, we perform a simulation approach to examine system capacity and related constraints through a sensitivity study is done. Finally, a hardware proof-of-concept, consisting of inexpensive and simple off-theshelf components, is built and tested. We have demonstrated that such a system is indeed feasible. However, the carrier frequency and code allocation must be changed to prevent interference with the current aircraft’s automatic surveillance system. The simulation and capacity study tests the limitation of such a system in high-density scenarios, and provide recommendation for additional work on hardware, format, and modulation techniques to enable such a system. Finally, the hardware test shows that an inexpensive commercial-of-the-shelf implementation with a range of approximately 200 meters is possible, on hardware drawing less than five Watts of power.